Identifying Clusters of Active Transportation Using Spatial Scan Statistics

Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
American journal of preventive medicine (Impact Factor: 4.53). 09/2009; 37(2):157-66. DOI: 10.1016/j.amepre.2009.04.021
Source: PubMed


There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation.
Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008.
Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC.
The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.


Available from: Linda Pickle
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    • "Even though STSS-based approaches have commonly been used in epidemiology to detect disease outbreaks (Kulldorff et al. 2005; Neill et al. 2005), in crime science to detect crime hotspots (Maciejewski et al. 2010; Nakaya and Yano 2010) amongst others (SaTScan 2010); their investigation in transportation science is a recent research endeavour. According to the best of our knowledge, only Huang et al. (2009) investigated the use of spatial scan statistics to detect clusters of active transportation (i.e. walking, cycling); however, they have not considered the temporal aspect of the phenomenon. "
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